Frizz and fuzz are common terms in the hair and textile industry, respectively (among others), to describe the presence of stray fibers amongst a substrate. The ability to measure these phenomena under a variety of lighting conditions is desirable, such as allowing a person to use a device, such as a smartphone or tablet as an evaluation tool. Such a tool could be used to help recommend a product, styling technique, or laundry machine settings (among others). In an environment where the lighting conditions are highly controlled, this can be accomplished using a common technique involving the use of backlighting and applying fundamental edge detection filters. The difficulty lies in extending this capability to a situation in which an object containing stray fibers is imaged against a contrasted background with little to no control over the surrounding illumination. In the example of imaging the frizz on a person, backlighting alone followed by fundamental edge detection algorithms can yield satisfactory results (an image in which only the fibers directly contrasted against the background are emphasized), however if any part of the face is illuminated from the front, contrasting regions of the face will be detected by the edge detection technique and create unwanted artifacts in the stray fiber image. The methodology described herein allows for the creation of a foundational stray fiber image taken under a variety of lighting conditions that can be subsequently analyzed for the detection, quantification, and virtual alteration of stray fibers when the starting image is one containing stray fibers emanating from an object/substrate and contrasted against a background.